Efficacy of BIO K+ CL1285® in the reduction of antibiotic-associated diarrhea – a placebo controlled double-blind randomized, multi-center study
Bibliographic record
Abstract
INTRODUCTION: Antibiotic associated diarrhea (AAD) is a frequently encountered adverse event following antibiotic administration. Evidence suggests that probiotics may be beneficial in preventing and decreasing the severity of AAD. MATERIAL AND METHODS: Adult patients who were prescribed antibiotics for 3-14 days were enrolled from eight Canadian centers. Study treatment was randomized at a 1 : 1 ratio of BIO-K+CL1285( (®) ) or placebo and was administered within 24 h of initiation to 5 days after termination of antibiotherapy. Patients were followed for 21 days after last dose of study treatment. The primary outcome was severity and incidence of AAD. Severity was measured by the total number of days with diarrhea and incidence was defined as the number of patients with at least one day with diarrhea over the total number of patients enrolled in the study. RESULTS: 216 patients were randomized to BIO-K+ and 221 to placebo. The mean (SD) number of days with diarrhea was 1.19. (3.20) days for the placebo and 0.67 (2.05) days for BIO-K+CL1285( (®) ) (p = 0.040). Adjusted multivariate linear regression results showed that the duration of diarrhea for BIO-K+CL1285 (®)vs. placebo was reduced by 51.5% (b[SE] = 0.515 [0.256], p = 0.045). The incidence of diarrhea was 21.8% for the BIO-K+ and 29.4% for the placebo group (OR = 0.667, p = 0.067). Multivariate logistic regression, showed that the adjusted odds ratio of AAD in patients receiving BIO-K+ vs. placebo was 0.627 (p = 0.037). Study treatment was well tolerated. CONCLUSIONS: BIO-K+ is effective for preventing and reducing the severity of AAD in patients receiving antibiotic therapy in a hospital setting.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".